# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import annotations

import unittest
from functools import partial
from itertools import product
from typing import TYPE_CHECKING, Any

import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest

import paddle.inference as paddle_infer

if TYPE_CHECKING:
    from collections.abc import Generator


class TrtConvertScaleTest(TrtLayerAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_configs(self):
        def generate_input1(attrs: list[dict[str, Any]], batch, is_int):
            if self.dims == 4:
                return np.ones([batch, 3, 24, 24]).astype(
                    np.int32 if is_int else np.float32
                )
            elif self.dims == 3:
                return np.ones([batch, 3, 24]).astype(
                    np.int32 if is_int else np.float32
                )
            elif self.dims == 2:
                return np.ones([batch, 24]).astype(
                    np.int32 if is_int else np.float32
                )
            elif self.dims == 1:
                return np.ones([24]).astype(np.int32 if is_int else np.float32)
            elif self.dims == 0:
                return np.ones([]).astype(np.int32 if is_int else np.float32)

        def generate_weight1(attrs: list[dict[str, Any]], is_int):
            return np.ones([1]).astype(np.int32 if is_int else np.float32)

        for (
            num_input,
            dims,
            batch,
            scale,
            bias,
            bias_after_scale,
            is_int,
        ) in product(
            [0, 1],
            [0, 1, 2, 3, 4],
            [1, 2],
            [0.1, -1.0],
            [0.0, 1.2],
            [False, True],
            [False, True],
        ):
            self.num_input = num_input
            self.dims = dims
            self.is_int = is_int
            dics = [
                {
                    "scale": scale,
                    "bias": bias,
                    "bias_after_scale": bias_after_scale,
                },
                {},
            ]

            dics_input = [
                {
                    "X": ["scale_input"],
                    "ScaleTensor": ["ScaleTensor"],
                },
                {"X": ["scale_input"]},
            ]
            dics_inputs = [
                {
                    "ScaleTensor": TensorConfig(
                        data_gen=partial(
                            generate_weight1,
                            dics,
                            is_int,
                        )
                    )
                },
                {},
            ]

            ops_config = [
                {
                    "op_type": "scale",
                    "op_inputs": dics_input[num_input],
                    "op_outputs": {"Out": ["scale_out"]},
                    "op_attrs": dics[0],
                }
            ]
            ops = self.generate_op_config(ops_config)
            program_config = ProgramConfig(
                ops=ops,
                weights=dics_inputs[num_input],
                inputs={
                    "scale_input": TensorConfig(
                        data_gen=partial(
                            generate_input1,
                            dics,
                            batch,
                            is_int,
                        )
                    )
                },
                outputs=["scale_out"],
                no_cast_list=["scale_input"] if is_int else [],
            )

            yield program_config

    def generate_dynamic_shape(self, attrs):
        if self.dims == 4:
            self.dynamic_shape.min_input_shape = {"scale_input": [1, 3, 24, 24]}
            self.dynamic_shape.max_input_shape = {"scale_input": [4, 3, 24, 24]}
            self.dynamic_shape.opt_input_shape = {"scale_input": [1, 3, 24, 24]}
        elif self.dims == 3:
            self.dynamic_shape.min_input_shape = {"scale_input": [1, 3, 24]}
            self.dynamic_shape.max_input_shape = {"scale_input": [4, 3, 24]}
            self.dynamic_shape.opt_input_shape = {"scale_input": [1, 3, 24]}
        elif self.dims == 2:
            self.dynamic_shape.min_input_shape = {"scale_input": [1, 24]}
            self.dynamic_shape.max_input_shape = {"scale_input": [9, 48]}
            self.dynamic_shape.opt_input_shape = {"scale_input": [1, 24]}
        elif self.dims == 1:
            self.dynamic_shape.min_input_shape = {"scale_input": [24]}
            self.dynamic_shape.max_input_shape = {"scale_input": [24]}
            self.dynamic_shape.opt_input_shape = {"scale_input": [24]}
        elif self.dims == 0:
            self.dynamic_shape.min_input_shape = {"scale_input": []}
            self.dynamic_shape.max_input_shape = {"scale_input": []}
            self.dynamic_shape.opt_input_shape = {"scale_input": []}
        return self.dynamic_shape

    def sample_predictor_configs(
        self, program_config, run_pir=False
    ) -> Generator[
        Any, Any, tuple[paddle_infer.Config, list[int], float] | None
    ]:
        def clear_dynamic_shape():
            self.dynamic_shape.min_input_shape = {}
            self.dynamic_shape.max_input_shape = {}
            self.dynamic_shape.opt_input_shape = {}

        def generate_trt_nodes_num(attrs, dynamic_shape):
            if not dynamic_shape and (self.dims == 1 or self.dims == 0):
                return 0, 3
            return 1, 2

        attrs = [
            program_config.ops[i].attrs for i in range(len(program_config.ops))
        ]

        # for static_shape
        clear_dynamic_shape()
        if not run_pir:
            self.trt_param.precision = paddle_infer.PrecisionType.Float32
            program_config.set_input_type(np.float32)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                1e-5,
            )
            self.trt_param.precision = paddle_infer.PrecisionType.Half
            program_config.set_input_type(np.float16)
            yield (
                self.create_inference_config(),
                generate_trt_nodes_num(attrs, False),
                (1e-3, 1e-3),
            )

        # for dynamic_shape
        self.generate_dynamic_shape(attrs)
        self.trt_param.precision = paddle_infer.PrecisionType.Float32
        program_config.set_input_type(np.float32)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            1e-5,
        )
        self.trt_param.precision = paddle_infer.PrecisionType.Half
        program_config.set_input_type(np.float16)
        yield (
            self.create_inference_config(),
            generate_trt_nodes_num(attrs, True),
            (1e-3, 1e-3),
        )

    def add_skip_trt_case(self):
        def teller1(program_config, predictor_config):
            if self.num_input == 0:
                return True
            return False

        self.add_skip_case(
            teller1,
            SkipReasons.TRT_NOT_SUPPORT,
            "INPUT ScaleTensor and Shape NOT SUPPORT",
        )

        def teller2(program_config, predictor_config):
            if self.is_int and len(self.dynamic_shape.min_input_shape) == 0:
                return True
            return False

        self.add_skip_case(
            teller2,
            SkipReasons.TRT_NOT_SUPPORT,
            "INTEGER INPUT OF STATIC SHAPE NOT SUPPORT",
        )

    def test(self):
        self.add_skip_trt_case()
        self.run_test(run_pir=True)


if __name__ == "__main__":
    unittest.main()
